SIEGE: Smoking Induced Epithelial Gene Expression Database

Bioinformatics Program, College of Engineering, Boston University, 44 Cummington Street, Boston, MA 02215, USA.
Nucleic Acids Research (Impact Factor: 9.11). 02/2005; 33(Database issue):D573-9. DOI: 10.1093/nar/gki035
Source: PubMed


The SIEGE (Smoking Induced Epithelial Gene Expression) database is a clinical resource for compiling and analyzing gene expression data from epithelial cells of the human intra-thoracic airway. This database supports a translational research study whose goal is to profile the changes in airway gene expression that are induced by cigarette smoke. RNA is isolated from airway epithelium obtained at bronchoscopy from current-, former- and never-smoker subjects, and hybridized to Affymetrix HG-U133A Genechips, which measure the level of expression of approximately 22,500 human transcripts. The microarray data generated along with relevant patient information is uploaded to SIEGE by study administrators using the database's web interface, found at PERL-coded scripts integrated with SIEGE perform various quality control functions including the processing, filtering and formatting of stored data. The R statistical package is used to import database expression values and execute a number of statistical analyses including t-tests, correlation coefficients and hierarchical clustering. Values from all statistical analyses can be queried through CGI-based tools and web forms found on the 'Search' section of the database website. Query results are embedded with graphical capabilities as well as with links to other databases containing valuable gene resources, including Entrez Gene, GO, Biocarta, GeneCards, dbSNP and the NCBI Map Viewer.

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    • "However, we found the intra-subject variability to be high as well. The availability of the Sridhar buccal dataset provided comparison data and along with the previous work from this group [7], also provided published lists of genes from buccal and nasal cells which change expression levels due to smoking. Gene lists developed from the current study did not overlap extensively with each other or with the Sridhar lists. "
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    ABSTRACT: Gene expression changes resulting from conditions such as disease, environmental stimuli, and drug use, can be monitored in the blood. However, a less invasive method of sample collection is of interest because of the discomfort and specialized personnel necessary for blood sampling especially if multiple samples are being collected. Buccal mucosa cells are easily collected and may be an alternative sample material for biomarker testing. A limited number of studies, primarily in the smoker/oral cancer literature, address this tissue's efficacy as an RNA source for expression analysis. The current study was undertaken to determine if total RNA isolated from buccal mucosa could be used as an alternative tissue source to assay relative gene expression. Total RNA was isolated from swabs, reverse transcribed and amplified. The amplified cDNA was used in RT-qPCR and microarray analyses to evaluate gene expression in buccal cells. Initially, RT-qPCR was used to assess relative transcript levels of four genes from whole blood and buccal cells collected from the same seven individuals, concurrently. Second, buccal cell RNA was used for microarray-based differential gene expression studies by comparing gene expression between a group of female smokers and nonsmokers. An amplification protocol allowed use of less buccal cell total RNA (50 ng) than had been reported previously with human microarrays. Total RNA isolated from buccal cells was degraded but was of sufficient quality to be used with RT-qPCR to detect expression of specific genes. We report here the finding of a small number of statistically significant differentially expressed genes between smokers and nonsmokers, using buccal cells as starting material. Gene Set Enrichment Analysis confirmed that these genes had a similar expression pattern to results from another study. Our results suggest that despite a high degree of degradation, RNA from buccal cells from cheek mucosa could be used to detect differential gene expression between smokers and nonsmokers. However the RNA degradation, increase in sample variability and microarray failure rate show that buccal samples should be used with caution as source material in expression studies.
    BMC Medical Genomics 06/2010; 3(1):24. DOI:10.1186/1755-8794-3-24 · 2.87 Impact Factor
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    • "General repositories such as GEO [5] and ArrayExpress [6] operate as central data distribution centres encompassing gene expression data from different organisms and from various conditions. In contrast, resources like CGED [7], SIEGE [8] and GeneAtlas [9] are specialized databases that address specific problems; CGED concentrates on gene expression in various human cancer tissues, SIEGE focuses on epithelial gene expression changes induced by smoking in humans and Gene Atlas provides the expression profiles of genes in various mouse and human tissues. "
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    ABSTRACT: The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina. RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated. The results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.
    BMC Genomics 02/2008; 9(1):208. DOI:10.1186/1471-2164-9-208 · 3.99 Impact Factor
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    • "Recently, a database has been created containing the gene expression profiles induced by smoking in the human airway transcriptome [7]. One recent paper, using quantitative real-time PCR, has described the variations in selected genes in mucosal Free Radical Biology & Medicine 43 (2007) 415 – 422 "
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    ABSTRACT: Healthy volunteers (n=50) were enrolled for studying the variation of gene expression induced by smoking in peripheral lymphocytes. RNAs from smokers (>3 cigarettes/day, n=20) and passive smokers (exposed to tobacco smoke >3 h/day, n=10) were hybridized versus a reference pool obtained by mixing equal amounts of RNA from 20 nonsmokers, and gene expression was analyzed using DNA microarrays containing 13,971 oligos. Principal component analysis showed that 99.7% of gene expression variability was related to plasma cotinine, age, and DNA oxidation damage. SAM and GenMAPP/MAPPFinder analyses showed that smokers, compared to nonsmokers, had 129 down-regulated and 87 up-regulated genes, whereas passive smokers, compared to nonsmokers, had 44 down-regulated and 159 up-regulated genes, mainly involved in pathways associated with the activation of defensive responses. Hierarchical cluster analysis identified two distinct clusters of smokers, characterized by different oxidative DNA damage: smokers with high DNA oxidation damage, compared to smokers with low DNA oxidation damage, had a large number (150) of down-regulated genes, mainly associated with xenobiotic metabolism, DNA damage and repair, inflammatory responses, lymphocyte activation, and cytokine activity, suggesting a reduced cellular response to toxic agents in this subset of smokers that could lead to an increased DNA oxidation damage.
    Free Radical Biology and Medicine 08/2007; 43(3):415-22. DOI:10.1016/j.freeradbiomed.2007.04.018 · 5.74 Impact Factor
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